{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_color_maual`\n",
    "`scale_color_manual` applies color values to discrete color variables in your ggplots. It has 1 parameter:\n",
    "\n",
    "- `values` - colors you'd like to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TJ6uzs1O7du3S3LlzJUlz5szRrl27TMYEAAAAThGIQn6yjo4OHT58WBdddJHS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe6a090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284426449)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_manual(values=['blue', 'green', 'purple'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+lJSUDB7HGA6HNXHiRHV3d2v37t2aN2+eJGnu3LnavXu3yZgAAADASXxRyE/U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\nBvnvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11010f810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285244233)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_manual(values=['MediumSeaGreen', 'MediumSlateBlue', 'MediumAquaMarine'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mBdvaZDmOJMlyHAXb2w0nAgAAYxGFHGNWJhaTa9uSJNe2lYnFzAYCAABjEltWMGY51dXq\njMcVbG8/uYccAAAgzyjkGLsCATk1NewbBwAARrFlBQAAADCIQg4AAAAYRCEHAAAADKKQAwAAAAb5\n4kOdx44dUzweVzKZlGVZqq2t1Q033KDNmzerpaVFpaWlkqTGxkbNnDnTcFoAAADgJF8U8kAgoMWL\nF2vixInq6+vTqlWrNH36dEnS/PnzddNNNxlOCAAAAJyZLwp5WVmZysrKJEnFxcWqrKxUIpEwnAoA\nAAA4N18U8lN1dXXpyJEjmjx5sg4dOqTt27dr165dmjRpkhYvXqxwOGw6IgAAADDIV4W8r69P69ev\n15IlS1RcXKy5c+eqvr5elmXprbfe0muvvabbb79dktTd3a2enp4h3x+JRBQK+eotGRQMBmWfuEy8\nnwzMy69zk5idlzE772J23uX32cGffDPdTCaj9evXa/bs2bryyislafDDnJJUW1url156afDrlpYW\nNTc3D3mO+vp6NTQ05CcwcqqiosJ0BIwQs/MuZuddzA4oLL4p5Js2bVJVVZVuvPHGwdsSicTg3vIP\nP/xQEyZMGLyvtrZWs2bNGvIckUhEXV1dSqfT+QmdR8XFxerr6zMdI+dCoZAqKip8OzeJ2XkZs/Mu\nZuddfp8d/MkXhfzQoUNqbW3VhAkT9Nxzz0k6fsRha2urjhw5IsuyFI1GtXTp0sHvKS8vV3l5+WnP\n1dHRIcdx8pY9X0KhkC9f14B0Ou3b18fsvIvZeRez8y6/zw7+5ItCftlll+mpp5467XbOHAcAAECh\n40qdAAAAgEG+WCEHAGUysltbFWxrUyYWk1NdLQVYcwAAFD7+tALgC3ZrqyqbmnTx44+rsqlJ9q5d\npiMBAEZBU1OTGhsb9dlnn53zsc3Nzdq3b995Pf/3vvc9dXd3D/vxDQ0N+utf/6rt27fr3//938/r\nZw2gkAPwhWBbm6wTH+SyHEfB9nbDiQAAufbJJ59Ikt566y2NHz/+nI/fvHmzPvroo2E9t+u6am9v\nVygUOuPBH2djWZYkad68eacdqT1cbFkB4AuZWEyubctyHLm2rUwsZjoSACDHnnzySb333ntqbGyU\ndPzEoEsuuUQvv/yyLMvSv/3bv+nVV19VOBzWs88+q9WrV2vjxo3asGGDfv3rX2v58uU6fPiwysrK\n9OKLL+rzzz/Xfffdp0mTJmnOnDkaN27c4HOnUil95zvf0ccffyzbtrV27Vrdf//9evXVVyVJCxcu\nVDwel+u6g/n+7u/+Tu+//77mzp17Xq+LQg7AF5zqanXG4wq2t5/cQw4A8JWf/OQn+ud//metXbtW\nlmUpEAjoySef1Ntvv62qqiq9//77evfddyUdX/F+4IEHdP311+vWW2/Vf/7nf2rKlCn6zW9+oxdf\nfFHPPvus7rvvPn388cd6++23FQwG9fjjj+vmm2+WJP3yl7/U3Llz9YMf/GDw5xcXF+svf/mL/vrX\nv+qSSy4ZvN7NgGnTpumDDz6gkAMYowIBOTU1cmpqTCcBAIyyjo4OPfroo+rq6tInn3yi2tpaffbZ\nZ/ryl788+BjLsoasXu/bt2+wKM+dO1dvvPGGJGn27NkKBoOn/YwPP/xQ3/3ud4fc9q1vfUsvvfSS\nksmk/uEf/iFnr4c95AAAAPAM13W1du1aLV26VJs3b9bixYvluq6uuuoqbdmyZcjjbNsevCrtjBkz\ntG3bNknS+++/P3i9moE94JI0a9YsHThwQJJ01VVXDe4JHyj2X//61/WHP/xBb775pr72ta+dlu3A\ngQO66qqrzvs1UcgBAADgGZZlqbGxUT/72c90xx13qLOzU5J07bXX6vrrr9f8+fPV2NioDz74QF/5\nyle0YsUK/eAHP9Add9yhw4cPq76+XuvWrdM//uM/Dj7fgNtuu01vvvmmpOOnrWzbtk0LFizQV7/6\nVUmSbdu68sorNXv2bAVOHK176vePZLuKJFnuqWv5UEdHhy8vuVtSUqLe3l7TMXLOtm1VVVX5dm4S\ns/MyZuddzM67/D47jL7vfe97WrFixVlPWvn+97+v+++/X9ddd92Q27dv364tW7bohz/84Xn/TPaQ\nAwAAACf88pe/POt9jz/+uLq7u08r49LxYw/nzZs3op9JIQcAAACG4ec///moPC97yAEAAACDKOQA\nAACAQRRyAAAAwCAKOQAAAGAQhRwAAACesnbtWk2YMMF0jJzhlBUAAACM3CkXxrlgw7g8Tjab1Suv\nvKLLLrssdz/XMFbIAQAA4Blr167V3XffPXilTD/wzysBAACAr2WzWW3YsEH33HOP/HSxeQo5AAAA\nPOHFF1/U3XffbTpGzlHIAQAA4AkffPCB1qxZoyVLlmjv3r168sknTUfKCT7UCeRKJiO7tVXBtjZl\nYjE51dWSj/a3FRzebwAoDHncOvLMM88M/vO8efP0s5/9LG8/ezRRyIEcsVtbVdnUJMtx5Nq2OuNx\nOTU1pmP5Fu83AIxt27dvNx0hZ1hOAnIk2NYmy3EkSZbjKNjebjiRv/F+AwD8gkIO5EgmFpNr25Ik\n17aVicXMBvI53m8AgF+wZQXIEae6Wp3xuILt7Sf3NGPU8H4DAPzCcv10iOMFSqVSSqVSvjrXckAg\nEFA2mzUdI+csy1JRUZH6+/t9OTeJ2XkZs/MuZuddfp5dNBo1HQOjhBXyU4TDYSUSCTkn9qX6SUlJ\niXp7e03HyDnbthWNRpVMJn05N4nZeRmz8y5m511+nh38a9iFfMqUKbIs67Tbi4uLdemll+rOO+/U\no48+qlCIjg8AAAAM17A/1Pn9739fFRUVeuqpp/SrX/1KP/7xjzV+/Hg98MADuueee/Tss8/qRz/6\n0WhmBQAAwBjX3NyshQsXqrGxUZs2bTIdJyeGvZy9evVqvfHGG5o0adLgbUuWLNFXv/pV/e///q8a\nGhq0cOFC/eQnPxmVoAAAAChA/3r6DooRe+qLP9uQSqW0YsUK/dd//ZevdmUMe4X8k08+USQSGXJb\naWmpPv74Y0nSFVdcoc8//zy36QAAAIAT3nvvPZWUlOjrX/+67rrrLn366aemI+XEsAv50qVLdfvt\nt+vNN9/U7t279eabb+quu+7S0qVLJR1/g2KcAwwAAIBR8pe//EX79+/Xq6++qu9+97t66qmnTEfK\niWEX8l/84he64YYb9PDDD6umpkYPPfSQ5s6dq+eee06SNH36dP3hD38YtaAAAAAY26LRqG6++WaF\nQiE1Njbqgw8+MB0pJ4a9+SYcDuuZZ57RM888c8b7v/SlL+UsFAAAAPC35s6dq5UrV0qSduzYoenT\npxtOlBvntRv+7bff1tq1a/Xxxx9r0qRJ+uY3v6nGxsbRygYAAIBCd44PYubS+PHjdccdd6i+vl6B\nQEC//vWv8/azR9Owt6ysWLFC3/zmN3XxxRfr7//+7zV+/Hjde++9WrFixWjmAwAAAAY9+uijam5u\n1jvvvKNp06aZjpMTw14hX7lypd5++21dc801g7d9+9vf1qJFi/TDH/5wVMIBY0omI7u1VcG2NmVi\nMTnV1VJg2L8zAwAAjzqvLSszZswY8vX06dPPePVOAOfPbm1VZVOTLMeRa9vqjMfl1NSYCeP1Xw6y\nGdmdrQom2pQpi8mpqpYsD+UHAIwpw/4T6umnn9aDDz6ovXv3qre3V3v27NFDDz2kf/3Xf1U2mx38\nH4CRCba1yXIcSZLlOAq2txvLMvDLwcWPP67KpibZu3YZyzISdmerKn/fpIvfflyVv2+S3eGt/ACA\nsWXYK+QPP/ywJGnt2rVDbv+P//gPPfzww3JdV5ZlKZPJ5DYhMEZkYjG5tj24Qp4xeK7/mX45MLZa\nPwLBRJus7In8WUfBRLucCd7JDwAYW4ZdyA8ePDiaOYAxz6muVmc8rmB7+8ltIoYU0i8HI5Epi8kN\n2LKyjtyArUxZzHQkAADOatiFPBqN6tlnn9WOHTvU09Mz5L7XX38958GAMScQkFNTUxAr0YX0y8FI\nOFXV6lwaVzDRfnIPOQAABWrYhXzZsmXKZDK64447VFJSMpqZAJhWQL8cjIgVkDOhhm0qAOAzruvq\nO9/5jvbv3y9J+tWvfqUrrrjCcKoLN+xC/t///d/q7OxUUVHRaOYBcCFMnC7CiSYAMLZtzuGJewu+\n+CJDO3fuVH9/v/70pz9p69atWrFihX7xi1/k7ucbMuxCXldXp927d6vaY391DYwlA6eLDOyd7lwa\nH/VVYhM/EwAwNl166aVy3eOl/ejRo6qqqjKcKDeGXchXr16tW2+9VTfccIMuueSSIff9+Mc/znkw\nAOfPxOkinGgCAMiXyspKhUIhXXnllerr69Of//xn05FyYtiF/F/+5V90+PBhxWIxdXd3D97OhYGA\nwmHidBFONAEA5Mvrr78u27a1e/du/c///I/+6Z/+SevWrTMd64INu5CvW7dOe/bs0cSJE0czD4AL\nYOJ0EU40AQDki+u6Gj9+vCTp4osvHrJI7GXDLuTTp0+XbdujmQXAhTJxuggnmgDA2HaOD2Lm0qJF\ni7R69WotWLBA/f39WrlyZd5+9mgadiH/9re/rdtuu01PPPHEaXvIv/KVr+Q8GAAAAHCqYDDoiy0q\nf2vYhfznP/+5JOlHP/rRkNsty9KBAwdymwoAAAAYI4ZdyA8ePDiaOQAAAIAxadiFvJAdO3ZM8Xhc\nyWRSlmXpuuuu04033qje3l5t2LBBx44dUzQa1bJlyxQOh03HBQAAAAb5opAHAgEtXrxYEydOVF9f\nn1atWqXLL79cO3fu1PTp01VXV6etW7dqy5YtWrRokem4AAAAwCBfXN+6rKxs8DjG4uJiVVZWqru7\nW7t379acOXMkSbNnz9bu3btNxgQAAABO44tCfqquri4dOXJEl156qZLJpCKRiKTjpT2ZTBpOBwAA\nAAzliy0rA/r6+rR+/XotWbJExcXFp91/6lVFu7u71dPTM+T+SCSiUMhXb8mgYDDoy3PkB+bl17lJ\nzM7LmJ13MTvv8vvs4E++mW4mk9H69es1e/ZsXXnllZKOF+yenh5FIhElEgmVlpYOPr6lpUXNzc1D\nnqO+vl4NDQ15zY3cqKioMB0BI8TsvIvZeRezAwqL5bpu/i6vNIo2btyocePG6Wtf+9rgbW+88YZK\nSkoGP9TZ29s7+KHOs62QZzIZpdPpvGbPh+LiYvX19ZmOkXOhUEgVFRXq6ury5dwkZudlzM67mJ13\n+X128CdfrJAfOnRIra2tmjBhgp577jlJUmNjo26++WZt2LBBO3bs0EUXXaRly5YNfk95ebnKy8tP\ne66Ojg45jpO37PkSCoV8+boGpNNp376+vM8um5Hd2apgok2ZspicqmrJGr2PmzA772J23sXsgMLi\ni0J+2WWX6amnnjrjfcuXL89zGsDb7M5WVf6+SVbWkRuw1bk0LmdCjelYAAD4lu9OWQFwYYKJNlnZ\n46tLVtZRMNFuOBEAAP5GIQcwRKYsJjdw/IQCN2ArUxYzGwgAAJ/zxZYVALnjVFWrc2lcwUT7yT3k\nAABg1FDIAQxlBeRMqGHfOAAAeUIhB4CRyPNpNDnhZmSnWhV02pSxY3LCHsgMAGMAhRwARsCLp9HY\nqVZVHm6SJUeubHVOicspKezMADAWsDQCACPgxdNogk6bLJ3ILEdBp/AzA8BYQCEHgBHw4mk0GTsm\nVycyy1bGjpkNBACQxJYVABgRL55G44Sr1TklrqDTfnIPOQDAOAo5AIyEF0+jsQJySmrYNw4ABYYt\nKwAAAIBBrJADwEhwhCAAIEco5AAwAhwhCADIFZZzAGAEOEIQAJArFHIAGAGOEAQA5ApbVgBgBDhC\nEACQKxRyABgJjhAEAOQIW1YAAAAAgyjkAAAAgEFsWQGAC5HNyO5sVTDRpkxZTE4V55EDAM4PhRwA\nLoDd2arK3zfJyjpyA7Y6l8blTGBfOQBg+FjGAYALEEy0ycqeOI886yiY4DxyAMD5oZADwEhkM7I/\n3Skr3a/P5/9/Skcmyw3YypTFTCcDAHgMW1YAYAT+dqvKZ4uelxsef3wPOQAA54EVcgAYgb/dqhJw\nEnImzOEDnQCA88afHAAwApmymNyALUlsVQEAXBC2rADACDhV1epcGlcw0X7yuEMAAEaAQg4AI2EF\n5Eyo4YhC3AWsAAASMElEQVRDAMAFs1zXdU2HKBSpVEqpVEp+fEsCgYCy2azpGDlnWZaKiorU39/v\ny7lJzM7LmJ13MTvv8vPsotGo6RgYJayQnyIcDiuRSMhxHNNRcq6kpES9vb2mY+ScbduKRqNKJpO+\nnJvE7LxsVGfnZmSnWhV02pSxY3LC+btCKLPzLmbnXbZtm46AUUQhBwAPslOtqjzcJEuOXNnqnBKX\nU8L2GQDwIk5ZAQAPCjptsnTi2EU5CjpcIRQAvIpCDgAelLFjcnXi2EXZytgxs4EAACPGlhUA8CAn\nXK3OKXEFnfaTe8gBAJ5EIQcAL7ICckpq2DcOAD7AlhUAAADAIAo5AAAAYBCFHAAAADCIQg4AAAAY\nRCEHAAAADOKUFQBA7rgZ2alWBZ22k8cxWqz9AMAXoZADAHLGTrWq8nCTLDlyZatzSpyjGQHgHFi2\nAADkTNBpkyVHkmTJUdBpN5wIAAofhRwAkDMZOyZXtiTJla2MHTMbCAA8gC0rAICcccLV6pwSV9Bp\nP7mHHADwhSjkAIDcsQJySmrYNw4A54EtKwAAAIBBFHIAAADAILasAPA2zr0GAHgchRyAp3HuNQDA\n61hGAuBpnHsNAPA6CjkAT+PcawCA17FlBYCnFfK519lsWnbvTva3AwC+EIUcgLcV8rnX3S3sbwcA\nnBNLNQAwSgLsbwcADIMvVsg3bdqkPXv2qLS0VI899pgkafPmzWppaVFpaakkqbGxUTNnzjQZE8AY\nkz2xv31ghZz97QCAM/FFIZ8zZ47mzZuneDw+5Pb58+frpptuMpQKgK8N5/zz8uvzv7+dc9kBwHN8\nUcinTp2qzz//3HQMAGPIcM4/DwSC6svz/nbOZQcA7/FFIT+b7du3a9euXZo0aZIWL16scDhsOhIA\nnzjT+efDLr6juIp9QbkAAEb4tpDPnTtX9fX1sixLb731ll577TXdfvvtg/d3d3erp6dnyPdEIhGF\nQv58S4LBoGzbNh0j5wbm5de5ScyuUGWLpw3ZH54tnnbanM42O6tnl8afsor92dRNciPX5y3XhfL6\n7IaD/995l99nB3/y7XQHPswpSbW1tXrppZeG3N/S0qLm5uYht9XX16uhoSEv+ZBbFRUVpiNghLw6\nu3R0obpD/09W3wG5xdMV/VK9QsMsAd3HDg9ZxS7KHFZ51RLjuc6XV2cHZgcUGt8Uctd1h3ydSCRU\nVlYmSfrwww81YcKEIffX1tZq1qxZQ26LRCLq6upSOp0e3bAGFBcXq6+vz3SMnAuFQqqoqPDt3CRm\nV9DCs4//T1L/GT7HcrbZWcEpQ1ax+4NT1NHRkbdcF8oXszsH/n/nXX6fHfzJF4X8lVdeUVtbm3p7\ne7Vy5Uo1NDTo4MGDOnLkiCzLUjQa1dKlS4d8T3l5ucrLy097ro6ODjmOk6/oeRMKhXz5ugak02nf\nvj5m511nnV3RtUNPXym6VvLgezAmZ+cTBTW7HH+mwu+zgz/5opB/4xvfOO22mho+xASgQBXy1UWB\nPONkIIArdQIAAIPOdDIQMNZQyAEAgDGZE1e0lcQVbTFm+WLLCgAA8CYnXJ3/K9oCBYZCDgD5xuXt\ngZP4TAVAIQeAfONDbACAU7EkAwB5xofYAACnopADQJ7xITYAwKnYsgIAecaH2AAAp6KQA0C+8SE2\nAMAp2LICAAAAGEQhBwAAAAyikAMAAAAGUcgBAAAAgyjkAAAAgEEUcgAAAMAgCjkAAABgEOeQA8BY\n4GZkp1oVdNpOXozIYk0GAAoBhRwAxgA71arKw02y5MiVrc4pcS5MBAAFguURABgDgk6bLDmSJEuO\ngk674UQAgAEUcgAYAzJ2TK5sSZIrWxk7ZjYQAGAQW1YAYAxwwtXqnBJX0Gk/uYccAFAQKOQAMBZY\nATklNewbB4ACxJYVAAAAwCAKOQAAAGAQhRwAAAAwiEIOAAAAGEQhBwAAAAyikAMAAAAGWa7ruqZD\nFIpUKqVUKiU/viWBQEDZbNZ0jJyzLEtFRUXq7+/35dwkZudlzM67mJ13+Xl20WjUdAyMEs4hP0U4\nHFYikZDjOKaj5FxJSYl6e3tNx8g527YVjUaVTCZ9OTeJ2XkZs/MuZuddfp4d/IstKwAAAIBBFHIA\nAADAIAo5AAAAYBCFHAAAADCIQg4AAAAYRCEHAAAADKKQAwAAAAZRyAEAAACDKOQAAACAQRRyAAAA\nwCAKOQAAAGAQhRwAAAAwiEIOAAAAGEQhBwAAAAyikAMAAAAGUcgBAAAAgyjkAAAAgEEUcgAAAMAg\nCjkAAABgEIUcAAAAMIhCDgAAABhEIQcAAAAMopADAAAABoVMBwAAAD7kZmSnWhV02pSxY3LC1ZI1\nwnXAXD4XUIAo5AAAIOfsVKsqDzfJkiNXtjqnxOWU1Bh/LqAQ+aKQb9q0SXv27FFpaakee+wxSVJv\nb682bNigY8eOKRqNatmyZQqHw4aTAgAwNgSdNllyJEmWHAWd9hGX6Fw+F1CIfPH3PXPmzNG3vvWt\nIbdt3bpV06dP1xNPPKFp06Zpy5YthtIBADD2ZOyYXNmSJFe2MnasIJ4LKES+KORTp05VSUnJkNt2\n796tOXPmSJJmz56t3bt3m4gGAMCY5ISr1TklrqNf+rk6p/z2+L7vAnguoBD5YsvKmSSTSUUiEUlS\nWVmZksmk4UQAAIwhVkBOSU1utpbk8rmAAuTbQv63LMsa8nV3d7d6enqG3BaJRBQK+fMtCQaDsm3b\ndIycG5iXX+cmMTsvY3bexey8y++zgz/5drqRSEQ9PT2KRCJKJBIqLS0dcn9LS4uam5uH3FZfX6+G\nhoZ8xkSOVFRUmI6AEWJ23sXsvIvZAYXFN4Xcdd0hX8+aNUs7d+5UXV2ddu3apVmzZg25v7a29rTb\nIpGIurq6lE6nRz1vvhUXF6uvr890jJwLhUKqqKjw7dwkZudlzM67mJ13+X128CdfFPJXXnlFbW1t\n6u3t1cqVK9XQ0KC6ujqtX79eO3bs0EUXXaRly5YN+Z7y8nKVl5ef9lwdHR1yHCdf0fMmFAr58nUN\nSKfTvn19zM67mJ13MTvv8vvs4E++KOTf+MY3znj78uXL85wEAAAAOD++OPYQAAAA8CoKOQAAAGAQ\nhRwAAAAwiEIOAAAAGEQhBwAAAAyikAMAAAAGUcgBAAAAgyjkAAAAgEEUcgAAAMAgCjkAAABgEIUc\nAAAAMIhCDgAAABhEIQcAAAAMopADAAAABlHIAQAAAIMo5AAAAIBBFHIAAADAIAo5AAAAYBCFHAAA\nADCIQg4AAAAYRCEHAAAADKKQAwAAAAZRyAEAAACDKOQAAACAQRRyAAAAwCAKOQAAAGAQhRwAAAAw\niEIOAAAAGGS5ruuaDlEoUqmUUqmU/PiWBAIBZbNZ0zFyzrIsFRUVqb+/35dzk5idlzE772J23uXn\n2UWjUdMxMEpCpgMUknA4rEQiIcdxTEfJuZKSEvX29pqOkXO2bSsajSqZTPpybhKz8zJm513Mzrv8\nPDv4F1tWAAAAAIMo5AAAAIBBFHIAAADAIAo5AAAAYBCFHAAAADCIQg4AAAAYRCEHAAAADKKQAwAA\nAAZRyAEAAACDKOQAAACAQRRyAAAAwCAKOQAAAGAQhRwAAAAwiEIOAAAAGEQhBwAAAAyikAMAAAAG\nUcgBAAAAgyjkAAAAgEEUcgAAAMAgCjkAAABgEIUcAAAAMIhCDgAAABhEIQcAAAAMopADAAAABlHI\nAQAAAINCpgOMtp/+9KcKh8OyLEuBQEAPPfSQ6UgAAADAIN8XcsuydP/996ukpMR0FAAAAOA0Y2LL\niuu6piMAAAAAZ+T7FXJJWrNmjQKBgGpra1VbW2s6DgAAADDI94X8wQcfVFlZmZLJpNasWaPKykpN\nnTpV3d3d6unpGfLYSCSiUMifb0kwGJRt26Zj5NzAvPw6N4nZeRmz8y5m511+nx38yXLH0H6OzZs3\nq6ioSDfddJPeeecdNTc3D7l/6tSpuuuuu1ReXm4oIc5Xd3e3WlpaVFtby9w8htl5F7PzLmbnXczO\n33z961Z/f79c11VxcbH6+/u1f/9+1dfXS5Jqa2s1a9aswcd2dHQoHo+rp6eHf9E9pKenR83NzZo1\naxZz8xhm513MzruYnXcxO3/zdSFPJpNat26dLMtSNpvVtddeqxkzZkiSysvL+RcaAAAAxvm6kFdU\nVOjRRx81HQMAAAA4qzFx7CEAAABQqIJPP/3006ZDFALXdVVUVKRYLKbi4mLTcTBMzM27mJ13MTvv\nYnbexez8bUydsnI2mzZt0p49e1RaWqrHHnvMdBwM07FjxxSPx5VMJmVZlq677jrdeOONpmNhGNLp\ntF544QVlMhlls1ldffXVWrBggelYGKZsNqtVq1apvLxc9957r+k4OA8//elPFQ6HZVmWAoGAHnro\nIdORMEypVEq/+93v9Omnn8qyLN1+++269NJLTcdCjvh6D/lwzZkzR/PmzVM8HjcdBechEAho8eLF\nmjhxovr6+rRq1SpdfvnlqqqqMh0N5xAKhbR8+XIVFRUpm83q+eef14wZM/jDxSO2bdumqqoq9fX1\nmY6C82RZlu6//36VlJSYjoLz9Mc//lEzZ87U3XffrUwmI8dxTEdCDrGHXMfPH+c/Tt5TVlamiRMn\nSpKKi4tVWVmpRCJhOBWGq6ioSNLx1fJsNivLsgwnwnAcO3ZMe/fu1XXXXWc6CkaIvxj3nlQqpUOH\nDqmmpkbS8YsfhcNhw6mQS6yQwxe6urp05MgRTZ482XQUDNPAtoejR49q3rx5zM4jXnvtNS1atIjV\ncQ9bs2aNAoGAamtrVVtbazoOhuHzzz/XuHHj9Nvf/lZHjhzRpEmTtGTJEl9ekXSsopDD8/r6+rR+\n/XotWbKED7p4SCAQ0COPPKJUKqV169bp008/1YQJE0zHwhcY+KzNxIkTdfDgQdNxMAIPPvigysrK\nlEwmtWbNGlVWVmrq1KmmY+EcstmsPvnkE916662aPHmy/vjHP2rr1q1qaGgwHQ05QiGHp2UyGa1f\nv16zZ8/WlVdeaToORiAcDmvatGnat28fhbzAHTp0SB999JH27t2rdDqtvr4+bdy4UXfeeafpaBim\nsrIySVJpaamuuuoq/d///R+F3AMGLmY48DeJV199tf785z8bToVcopCfwJ46b9q0aZOqqqo4XcVj\nksnk4B5Ix3G0f/9+1dXVmY6Fc1i4cKEWLlwoSWpra9O7775LGfeQ/v5+ua6r4uJi9ff3a//+/aqv\nrzcdC8MQiUR00UUXqbOzU5WVlTp48CAHGPgMhVzSK6+8ora2NvX29mrlypVqaGgY/OAECtehQ4fU\n2tqqCRMm6LnnnpMkNTY2aubMmYaT4Vx6enoUj8fluq5c19U111yjK664wnQswNeSyaTWrVsny7KU\nzWZ17bXXasaMGaZjYZiWLFmijRs3KpPJqKKiQk1NTaYjIYc4hxwAAAAwiGMPAQAAAIMo5AAAAIBB\nFHIAAADAIAo5AAAAYBCFHAAAADCIQg4AAAAYRCEHAAAADKKQAwAAAAZRyAEAAACDKOQAAACAQRRy\nAAAAwCAKOQAAAGAQhRwAAAAwiEIOAAAAGEQhBwAAAAyikAMAAAAGUcgBAAAAgyjkAAAAgEEUcgAA\nAMAgCjkA+EwgENCBAwdMxwAADBOFHAB8xrIs0xEAAOeBQg4AHrF69Wrddtttg1/PnDlT99xzz+DX\nU6ZMUTQalSRVV1ervLxcGzZsyHtOAMD5sVzXdU2HAACc28GDB1VbW6ujR4/qk08+0fz585XNZnXo\n0CEdOHBAc+fO1WeffaZAIKD9+/dr2rRppiMDAIYhZDoAAGB4pk2bprKyMu3cuVMfffSRFi9erF27\ndmnPnj1699139eUvf3nwsay1AIB3UMgBwEPq6+v1zjvvaN++fVqwYIEqKiq0efNmvffee6qvrzcd\nDwAwAuwhBwAPueWWW7R582Zt3bpV9fX1uuWWW9Tc3Kw//elPWrBggel4AIARYA85AHjI3r17VVtb\nqy996Uvas2ePEomEYrGYMpmMurq6ZFmWJk2apDVr1mjhwoWm4wIAhoEVcgDwkJkzZ6qsrEy33HKL\nJKmsrEyXX3656urqBo87fPrpp3Xffffp4osv1iuvvGIyLgBgGFghBwAAAAxihRwAAAAwiEIOAAAA\nGEQhBwAAAAyikAMAAAAGUcgBAAAAgyjkAAAAgEEUcgAAAMAgCjkAAABgEIUcAAAAMOj/BwRw7DTI\nt59zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110160bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285310097)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_manual(values=['#ff0000', '#ff8000', '#ffbf00'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
